Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method
The world is currently being hit by the COVID-19 virus. In this New Normal era, a rule is enforced that everyone must wear a mask wherever we are. Checking masks and body temperature is still done manually or by human observation, thus allowing for inaccuracies in observing and checking temperature....
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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/104/e3sconf_icstunkhair2021_02009.pdf |
| _version_ | 1818700958943674368 |
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| author | Joni Koko Rahmawati Anita Ima Sukri Hanifudin |
| author_facet | Joni Koko Rahmawati Anita Ima Sukri Hanifudin |
| author_sort | Joni Koko |
| collection | DOAJ |
| description | The world is currently being hit by the COVID-19 virus. In this New Normal era, a rule is enforced that everyone must wear a mask wherever we are. Checking masks and body temperature is still done manually or by human observation, thus allowing for inaccuracies in observing and checking temperature. The problem occurred at Trunojoyo Madura University which still uses a manual mask and body temperature checking system. So, for accuracy and to reduce the risk of contracting officers. A tool was created to detect the mask and temperature automatically. In this study using a camera, temperature sensor MLX90614, and proximity sensor using Raspberry Pi. This research uses a machine learning system with the Deep Learning Convolutional Neural Network (CNN) Single Shot Detector (SSD) method. From this study, the results of mask detection obtained a success percentage of 93.4% and an error percentage of 6.6% from the entire test and obtained an average detection time of 2.63 seconds. And the average time of the whole system is 3.8 seconds. In this study, there was a delay during detection due to the heavy computational load on the system, so for further research, use a mini pc that has better performance. |
| first_indexed | 2024-12-17T15:13:13Z |
| format | Article |
| id | doaj.art-c920fc0a1fe64c0e924c81b417a4c62b |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-17T15:13:13Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c920fc0a1fe64c0e924c81b417a4c62b2022-12-21T21:43:37ZengEDP SciencesE3S Web of Conferences2267-12422021-01-013280200910.1051/e3sconf/202132802009e3sconf_icstunkhair2021_02009Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network MethodJoni KokoRahmawati Anita ImaSukri HanifudinThe world is currently being hit by the COVID-19 virus. In this New Normal era, a rule is enforced that everyone must wear a mask wherever we are. Checking masks and body temperature is still done manually or by human observation, thus allowing for inaccuracies in observing and checking temperature. The problem occurred at Trunojoyo Madura University which still uses a manual mask and body temperature checking system. So, for accuracy and to reduce the risk of contracting officers. A tool was created to detect the mask and temperature automatically. In this study using a camera, temperature sensor MLX90614, and proximity sensor using Raspberry Pi. This research uses a machine learning system with the Deep Learning Convolutional Neural Network (CNN) Single Shot Detector (SSD) method. From this study, the results of mask detection obtained a success percentage of 93.4% and an error percentage of 6.6% from the entire test and obtained an average detection time of 2.63 seconds. And the average time of the whole system is 3.8 seconds. In this study, there was a delay during detection due to the heavy computational load on the system, so for further research, use a mini pc that has better performance.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/104/e3sconf_icstunkhair2021_02009.pdfcovid-19smart door lockneural network method |
| spellingShingle | Joni Koko Rahmawati Anita Ima Sukri Hanifudin Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method E3S Web of Conferences covid-19 smart door lock neural network method |
| title | Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method |
| title_full | Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method |
| title_fullStr | Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method |
| title_full_unstemmed | Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method |
| title_short | Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method |
| title_sort | design and build a smart door lock using the deep learning convolutional neural network method |
| topic | covid-19 smart door lock neural network method |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/104/e3sconf_icstunkhair2021_02009.pdf |
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